Security Vulnerabilities Detection in Cloud Computing: A Focus on Network and Virtual Machine Security

Abstract

The rapid adoption of cloud computing has fundamentally transformed IT infrastructure, enabling on-demand scalability, cost-efficiency, and resource sharing across multi-tenant environments. However, this paradigm shift introduces a complex and evolving threat landscape, particularly concerning network-level vulnerabilities, virtual machine (VM) security, and side-channel attacks. This paper presents a comprehensive survey of security vulnerabilities inherent to cloud computing environments, with a particular emphasis on detection methodologies targeting network-layer threats and VM-based exploits. We systematically review classical and machine learning-based intrusion detection systems (IDS), hypervisor security mechanisms, and cross-VM attack vectors including cache-based and timing side-channel attacks. Furthermore, we evaluate the effectiveness of anomaly detection frameworks, network traffic analysis tools, and VM isolation enforcement strategies. Our analysis draws on seminal research published prior to 2020 and identifies key gaps, challenges, and directions for future work in cloud security detection. The findings indicate that a multi-layered detection approach combining behavioral profiling, cryptographic enforcement, and resource monitoring yields the most robust defense posture for cloud deployments.